Common everyday environments are populated with both animate (living) and inanimate (non-living) objects. Under most conditions, both people and animals can view such an environment and quickly distinguish animate and inanimate objects from each other. A variety of cues, including object recognition, motion, audio, and social context are used in these assessments.
By comparison, the technological equivalent, for example, real-time automated sensing systems relying on cameras and microphones, and appropriate interpretive programs and databases are neither as efficient nor versatile as a human at this type of task. Additionally, there are complicating circumstances which can make the sensing and classification task much more difficult, particularly for an automated system, and on occasion, even for people. For example, some inanimate objects (such as stuffed animals or card-board cut-outs of people) can be particularly difficult to detect correctly as inanimate, depending on the time and other cues available. Likewise, hidden or obscured objects (people or animals in difficult poses or clothing (towel over their head)), stationary people, or projected images of people can be particularly difficult, unreliable, or time consuming, for imaging techniques (including face detection, body shape detection, motion detection) alone to correctly distinguish animate and inanimate objects from one another.
Accordingly, a need in the art exists for improved techniques for detecting and classifying animate (living) or inanimate (non-living) objects.